aqora

An FPGA Implementation of Fast 2D-Ising-Model Solver for the Max-Cut Problem

英範業天, 正之廣本, 高史佐藤

IEICE technical report. SpeechFeb 29, 20162 citations

A Coherent Ising Machine for MAX-CUT Problems: Performance Evaluation against Semidefinite Programming and Simulated Annealing

Yoshitaka Haribara, Shoko Utsunomiya, Y. Yamamoto

arXiv:1501.07030Lecture notes in physicsDec 29, 201564 citations

Benchmarking coherent Ising machines and quantum annealers with MAX-CUT and SK problems

Ryan Hamerly, Takahiro Inagaki, Peter L. McMahon +10 more

Bulletin of the American Physical SocietyMar 6, 20190 citations

Utilizing Graph Sparsification for Pre-processing in Max Cut QUBO Solver

Vorapong Suppakitpaisarn, Jin‐Kao Hao

arXiv:2401.13004Lecture notes in computer scienceJan 1, 20241 citation

Design and FPGA Realization of QUBO hardware accelerator for MAX-CUT problem

Maciej Lewandowski, Piotr Dudek

Combinatorial optimization problems are fundamental to many industrial and scientific applications, but are often NP-hard, requiring heuristic and probabilistic approaches to find good solutions fast. This paper presents the first hardware implementation of the neuromorphic-inspired NeuroSA algorithm, for accelerating Quadratic Unconstrained Binary Optimization (QUBO) problems. We show that it is possible to run a standard QUBO benchmark problem with 800 variables on a low-cost FPGA with a clock speed of approximately 144 MHz, which converges to the same solution value found by the numerical solver.Combinatorial optimization problems are fundamental to many industrial and scientific applications, but are often NP-hard, requiring heuristic and probabilistic approaches to find good solutions fast. This paper presents the first hardware implementation of the neuromorphic-inspired NeuroSA algorithm, for accelerating Quadratic Unconstrained Binary Optimization (QUBO) problems. We show that it is possible to run a standard QUBO benchmark problem with 800 variables on a low-cost FPGA with a clock speed of approximately 144 MHz, which converges to the same solution value found by the numerical solver.

IEEE International Symposium on Circuits and Systems proceedingsMay 25, 20252 citations

Hybrid Quantum-Classical Multilevel Approach for Maximum Cuts on Graphs

Anthony Angone, Xiaoyuan Liu, Ruslan Shaydulin +1 more

Combinatorial optimization is one of the fields where near term quantum devices are being utilized with hybrid quantum-classical algorithms to demonstrate potentially practical applications of quantum computing. One of the most well studied problems in combinatorial optimization is the Max-Cut problem. The problem is also highly relevant to quantum and other types of “post Moore” architectures due to its similarity with the Ising model and other reasons. In this paper, we introduce a scalable hybrid multilevel approach to solve large instances of Max-Cut using both classical only solvers and quantum approximate optimization algorithm (QAOA). We compare the results of our solver to existing state of the art large-scale Max-Cut solvers. We demonstrate excellent performance of both classical and hybrid quantum-classical approaches and show that using QAOA within our framework is comparable to classical approaches. Reproducibility: Our solver is publicly available at https://github.com/angone/MLMax-cut.Combinatorial optimization is one of the fields where near term quantum devices are being utilized with hybrid quantum-classical algorithms to demonstrate potentially practical applications of quantum computing. One of the most well studied problems in combinatorial optimization is the Max-Cut problem. The problem is also highly relevant to quantum and other types of “post Moore” architectures due to its similarity with the Ising model and other reasons. In this paper, we introduce a scalable hybrid multilevel approach to solve large instances of Max-Cut using both classical only solvers and quantum approximate optimization algorithm (QAOA). We compare the results of our solver to existing state of the art large-scale Max-Cut solvers. We demonstrate excellent performance of both classical and hybrid quantum-classical approaches and show that using QAOA within our framework is comparable to classical approaches. Reproducibility: Our solver is publicly available at https://github.com/angone/MLMax-cut.

arXiv:2309.08815IEEE High Performance Extreme Computing ConferenceSep 25, 202313 citations

An FPGA Implementation of Fast 2D-Ising-Model Solver for the Max-Cut Problem

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